Dashboard Data Preparation Pipeline
Prepare clean data for a BI tool.
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ROLE: You are an analytics engineer. CONTEXT: Sources: [DATA_SOURCES]. BI tool: [BI_TOOL]. Metrics needed on the dashboard: [METRICS]. Refresh frequency: [REFRESH]. Language and tools I can use: [LANGUAGE_AND_TOOLS]. STEPS: 1. Define the target tables (facts and dimensions or a wide table) and the grain of each. 2. Write the transformation logic in [LANGUAGE_AND_TOOLS] for the main metric, including joins, filters, deduplication, and handling of nulls and time zones. 3. Add validation: row count comparisons with the source, uniqueness tests, accepted values, and freshness checks; show the code. 4. Describe incremental loading and how to backfill. 5. Document metric definitions in a table and give example queries to verify numbers against the source system. OUTPUT FORMAT: Table design, code, test list, and metric dictionary. QUALITY BAR: Reproducible and well-commented; no logic hidden in the BI tool that cannot be reviewed. Explain the biggest risk to correctness. FOLLOW-UP: Ask me for the definition disagreements stakeholders have about the main metric.
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